ANCOVA in
Reading Comprehension (Reading Comprehension)
Geiser C. Challco geiser@alumni.usp.br
NOTE:
- Teste ANCOVA para determinar se houve diferenças significativas no
Reading Comprehension (medido usando pre- e pos-testes).
- ANCOVA test to determine whether there were significant differences
in Reading Comprehension (measured using pre- and post-tests).
Setting Initial Variables
dv = "score.compreensao"
dv.pos = "score.compreensao.pos"
dv.pre = "score.compreensao.pre"
fatores2 <- c("genero","zona.participante","zona.escola","score.compreensao.quintile")
lfatores2 <- as.list(fatores2)
names(lfatores2) <- fatores2
fatores1 <- c("grupo", fatores2)
lfatores1 <- as.list(fatores1)
names(lfatores1) <- fatores1
lfatores <- c(lfatores1)
color <- list()
color[["prepost"]] = c("#ffee65","#f28e2B")
color[["grupo"]] = c("#bcbd22","#fd7f6f")
color[["genero"]] = c("#FF007F","#4D4DFF")
color[["zona.escola"]] = c("#AA00FF","#00CCCC")
color[["zona.participante"]] = c("#AA00FF","#00CCCC")
level <- list()
level[["grupo"]] = c("Controle","Experimental")
level[["genero"]] = c("F","M")
level[["zona.escola"]] = c("Rural","Urbana")
level[["zona.participante"]] = c("Rural","Urbana")
# ..
ymin <- 0
ymax <- 0
ymin.ci <- 0
ymax.ci <- 0
color[["grupo:genero"]] = c(
"Controle:F"="#ff99cb", "Controle:M"="#b7b7ff",
"Experimental:F"="#FF007F", "Experimental:M"="#4D4DFF",
"Controle.F"="#ff99cb", "Controle.M"="#b7b7ff",
"Experimental.F"="#FF007F", "Experimental.M"="#4D4DFF"
)
color[["grupo:zona.escola"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
color[["grupo:zona.participante"]] = c(
"Controle:Rural"="#b2efef","Controle:Urbana"="#e5b2ff",
"Experimental:Rural"="#00CCCC", "Experimental:Urbana"="#AA00FF",
"Controle.Rural"="#b2efef","Controle.Urbana"="#e5b2ff",
"Experimental.Rural"="#00CCCC", "Experimental.Urbana"="#AA00FF"
)
for (coln in c(
"palavras.lidas","score.compreensao","tri.compreensao",
"score.vocab","tri.vocab",
"score.vocab.ensinado","tri.vocab.ensinado","score.vocab.nao.ensinado","tri.vocab.nao.ensinado",
"score.CLPP","tri.CLPP","score.CR","tri.CR",
"score.CI","tri.CI","score.TV","tri.TV","score.TF","tri.TF","score.TO","tri.TO")) {
color[[paste0(coln,".quintile")]] = c("#BF0040","#FF0000","#800080","#0000FF","#4000BF")
level[[paste0(coln,".quintile")]] = c("1st quintile","2nd quintile","3rd quintile","4th quintile","5th quintile")
color[[paste0("grupo:",coln,".quintile")]] = c(
"Experimental.1st quintile"="#BF0040", "Controle.1st quintile"="#d8668c",
"Experimental.2nd quintile"="#FF0000", "Controle.2nd quintile"="#ff7f7f",
"Experimental.3rd quintile"="#8fce00", "Controle.3rd quintile"="#ddf0b2",
"Experimental.4th quintile"="#0000FF", "Controle.4th quintile"="#b2b2ff",
"Experimental.5th quintile"="#4000BF", "Controle.5th quintile"="#b299e5",
"Experimental:1st quintile"="#BF0040", "Controle:1st quintile"="#d8668c",
"Experimental:2nd quintile"="#FF0000", "Controle:2nd quintile"="#ff7f7f",
"Experimental:3rd quintile"="#8fce00", "Controle:3rd quintile"="#ddf0b2",
"Experimental:4th quintile"="#0000FF", "Controle:4th quintile"="#b2b2ff",
"Experimental:5th quintile"="#4000BF", "Controle:5th quintile"="#b299e5")
}
gdat <- read_excel("../data/data.xlsx", sheet = "triagem.st")
dat <- gdat
dat$grupo <- factor(dat[["grupo"]], level[["grupo"]])
for (coln in c(names(lfatores))) {
dat[[coln]] <- factor(dat[[coln]], level[[coln]][level[[coln]] %in% unique(dat[[coln]])])
}
dat <- dat[which(!is.na(dat[[dv.pre]]) & !is.na(dat[[dv.pos]])),]
dat <- dat[,c("id",names(lfatores),dv.pre,dv.pos)]
dat.long <- rbind(dat, dat)
dat.long$time <- c(rep("pre", nrow(dat)), rep("pos", nrow(dat)))
dat.long$time <- factor(dat.long$time, c("pre","pos"))
dat.long[[dv]] <- c(dat[[dv.pre]], dat[[dv.pos]])
for (f in c("grupo", names(lfatores))) {
if (is.null(color[[f]]) && length(unique(dat[[f]])) > 0)
color[[f]] <- distinctColorPalette(length(unique(dat[[f]])))
}
for (f in c(fatores2)) {
if (is.null(color[[paste0("grupo:",f)]]) && length(unique(dat[[f]])) > 0)
color[[paste0("grupo:",f)]] <- distinctColorPalette(length(unique(dat[["grupo"]]))*length(unique(dat[[f]])))
}
ldat <- list()
laov <- list()
lpwc <- list()
lemms <- list()
Descriptive Statistics
of Initial Data
df <- get.descriptives(dat, c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1)
get.descriptives(dat, c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
## There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.compreensao.pre |
45 |
3.378 |
3.0 |
0 |
9 |
1.527 |
0.228 |
0.459 |
2.00 |
NO |
0.864 |
2.410 |
| Experimental |
|
|
|
|
score.compreensao.pre |
37 |
2.865 |
2.0 |
0 |
6 |
1.619 |
0.266 |
0.540 |
1.00 |
NO |
0.633 |
-0.613 |
|
|
|
|
|
score.compreensao.pre |
82 |
3.146 |
3.0 |
0 |
9 |
1.580 |
0.174 |
0.347 |
2.00 |
NO |
0.708 |
0.959 |
| Controle |
|
|
|
|
score.compreensao.pos |
45 |
3.511 |
3.0 |
0 |
9 |
1.792 |
0.267 |
0.538 |
2.00 |
NO |
0.573 |
0.436 |
| Experimental |
|
|
|
|
score.compreensao.pos |
37 |
3.189 |
3.0 |
1 |
9 |
1.745 |
0.287 |
0.582 |
2.00 |
NO |
0.938 |
1.321 |
|
|
|
|
|
score.compreensao.pos |
82 |
3.366 |
3.0 |
0 |
9 |
1.767 |
0.195 |
0.388 |
2.00 |
NO |
0.743 |
0.838 |
| Controle |
F |
|
|
|
score.compreensao.pre |
24 |
3.458 |
3.0 |
0 |
9 |
1.744 |
0.356 |
0.736 |
1.25 |
NO |
0.915 |
2.243 |
| Controle |
M |
|
|
|
score.compreensao.pre |
21 |
3.286 |
3.0 |
1 |
6 |
1.271 |
0.277 |
0.578 |
2.00 |
YES |
0.321 |
-0.766 |
| Experimental |
F |
|
|
|
score.compreensao.pre |
18 |
2.833 |
2.0 |
1 |
6 |
1.543 |
0.364 |
0.768 |
1.00 |
NO |
1.078 |
-0.437 |
| Experimental |
M |
|
|
|
score.compreensao.pre |
19 |
2.895 |
3.0 |
0 |
6 |
1.729 |
0.397 |
0.833 |
2.00 |
YES |
0.275 |
-0.938 |
| Controle |
F |
|
|
|
score.compreensao.pos |
24 |
4.208 |
4.0 |
2 |
9 |
1.793 |
0.366 |
0.757 |
2.00 |
NO |
0.654 |
0.015 |
| Controle |
M |
|
|
|
score.compreensao.pos |
21 |
2.714 |
3.0 |
0 |
6 |
1.454 |
0.317 |
0.662 |
2.00 |
YES |
0.104 |
-0.626 |
| Experimental |
F |
|
|
|
score.compreensao.pos |
18 |
3.667 |
3.0 |
1 |
9 |
1.782 |
0.420 |
0.886 |
1.00 |
NO |
1.361 |
2.025 |
| Experimental |
M |
|
|
|
score.compreensao.pos |
19 |
2.737 |
2.0 |
1 |
6 |
1.628 |
0.373 |
0.784 |
3.00 |
YES |
0.403 |
-1.261 |
| Controle |
|
Rural |
|
|
score.compreensao.pre |
12 |
3.417 |
3.5 |
2 |
5 |
0.900 |
0.260 |
0.572 |
1.00 |
YES |
-0.116 |
-1.093 |
| Controle |
|
Urbana |
|
|
score.compreensao.pre |
23 |
3.696 |
3.0 |
1 |
9 |
1.743 |
0.364 |
0.754 |
2.50 |
NO |
1.040 |
1.376 |
| Controle |
|
|
|
|
score.compreensao.pre |
10 |
2.600 |
3.0 |
0 |
5 |
1.430 |
0.452 |
1.023 |
1.00 |
YES |
-0.181 |
-0.856 |
| Experimental |
|
Rural |
|
|
score.compreensao.pre |
15 |
3.267 |
3.0 |
1 |
6 |
1.831 |
0.473 |
1.014 |
3.00 |
YES |
0.415 |
-1.510 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pre |
12 |
2.167 |
2.0 |
0 |
6 |
1.467 |
0.423 |
0.932 |
0.50 |
NO |
1.165 |
1.368 |
| Experimental |
|
|
|
|
score.compreensao.pre |
10 |
3.100 |
3.0 |
1 |
5 |
1.287 |
0.407 |
0.920 |
1.50 |
YES |
0.118 |
-1.209 |
| Controle |
|
Rural |
|
|
score.compreensao.pos |
12 |
3.667 |
4.0 |
2 |
5 |
0.985 |
0.284 |
0.626 |
1.00 |
YES |
-0.427 |
-1.031 |
| Controle |
|
Urbana |
|
|
score.compreensao.pos |
23 |
4.000 |
4.0 |
1 |
9 |
2.067 |
0.431 |
0.894 |
3.50 |
YES |
0.443 |
-0.576 |
| Controle |
|
|
|
|
score.compreensao.pos |
10 |
2.200 |
2.5 |
0 |
4 |
1.229 |
0.389 |
0.879 |
1.75 |
YES |
-0.336 |
-1.261 |
| Experimental |
|
Rural |
|
|
score.compreensao.pos |
15 |
3.533 |
3.0 |
1 |
9 |
1.995 |
0.515 |
1.105 |
2.00 |
NO |
1.264 |
1.200 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pos |
12 |
2.917 |
3.0 |
1 |
5 |
1.443 |
0.417 |
0.917 |
2.25 |
YES |
-0.035 |
-1.433 |
| Experimental |
|
|
|
|
score.compreensao.pos |
10 |
3.000 |
3.0 |
1 |
6 |
1.764 |
0.558 |
1.262 |
2.75 |
YES |
0.219 |
-1.471 |
| Controle |
|
|
Rural |
|
score.compreensao.pre |
13 |
4.000 |
4.0 |
1 |
9 |
1.826 |
0.506 |
1.103 |
1.00 |
NO |
1.213 |
1.929 |
| Controle |
|
|
Urbana |
|
score.compreensao.pre |
32 |
3.125 |
3.0 |
0 |
6 |
1.338 |
0.237 |
0.482 |
2.00 |
YES |
0.093 |
-0.434 |
| Experimental |
|
|
Rural |
|
score.compreensao.pre |
12 |
3.000 |
3.0 |
1 |
6 |
1.414 |
0.408 |
0.899 |
1.25 |
NO |
0.707 |
-0.542 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pre |
25 |
2.800 |
2.0 |
0 |
6 |
1.732 |
0.346 |
0.715 |
1.00 |
NO |
0.619 |
-0.813 |
| Controle |
|
|
Rural |
|
score.compreensao.pos |
13 |
3.923 |
4.0 |
1 |
7 |
1.605 |
0.445 |
0.970 |
2.00 |
YES |
0.114 |
-0.686 |
| Controle |
|
|
Urbana |
|
score.compreensao.pos |
32 |
3.344 |
3.0 |
0 |
9 |
1.860 |
0.329 |
0.671 |
2.00 |
NO |
0.763 |
0.758 |
| Experimental |
|
|
Rural |
|
score.compreensao.pos |
12 |
3.417 |
3.5 |
1 |
6 |
1.379 |
0.398 |
0.876 |
1.25 |
YES |
0.063 |
-0.844 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pos |
25 |
3.080 |
3.0 |
1 |
9 |
1.913 |
0.383 |
0.790 |
2.00 |
NO |
1.125 |
1.364 |
| Controle |
|
|
|
1st quintile |
score.compreensao.pre |
3 |
0.667 |
1.0 |
0 |
1 |
0.577 |
0.333 |
1.434 |
0.50 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pre |
23 |
2.609 |
3.0 |
2 |
3 |
0.499 |
0.104 |
0.216 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pre |
18 |
4.500 |
4.0 |
4 |
6 |
0.618 |
0.146 |
0.307 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
5th quintile |
score.compreensao.pre |
1 |
9.000 |
9.0 |
9 |
9 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.compreensao.pre |
6 |
0.833 |
1.0 |
0 |
1 |
0.408 |
0.167 |
0.428 |
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pre |
22 |
2.409 |
2.0 |
2 |
3 |
0.503 |
0.107 |
0.223 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pre |
9 |
5.333 |
5.0 |
4 |
6 |
0.707 |
0.236 |
0.544 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
1st quintile |
score.compreensao.pos |
3 |
3.000 |
3.0 |
2 |
4 |
1.000 |
0.577 |
2.484 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pos |
23 |
3.087 |
3.0 |
0 |
6 |
1.676 |
0.350 |
0.725 |
2.00 |
YES |
0.202 |
-0.866 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pos |
18 |
3.944 |
4.0 |
1 |
9 |
1.830 |
0.431 |
0.910 |
2.00 |
NO |
0.838 |
0.944 |
| Controle |
|
|
|
5th quintile |
score.compreensao.pos |
1 |
7.000 |
7.0 |
7 |
7 |
|
|
|
0.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
1st quintile |
score.compreensao.pos |
6 |
2.167 |
2.0 |
1 |
3 |
0.753 |
0.307 |
0.790 |
0.75 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pos |
22 |
3.227 |
3.0 |
1 |
9 |
1.926 |
0.411 |
0.854 |
2.00 |
NO |
1.067 |
1.359 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pos |
9 |
3.778 |
4.0 |
1 |
6 |
1.563 |
0.521 |
1.202 |
2.00 |
YES |
-0.374 |
-1.164 |
ANCOVA and Pairwise
for one factor: grupo
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]),], "score.compreensao.pos", "grupo")
pdat.long <- rbind(pdat[,c("id","grupo")], pdat[,c("id","grupo")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo)
laov[["grupo"]] <- get_anova_table(aov)
pwc <- emmeans_test(pdat, score.compreensao.pos ~ grupo, covariate = score.compreensao.pre,
p.adjust.method = "bonferroni")
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, "grupo"),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- plyr::rbind.fill(pwc, pwc.long)
ds <- get.descriptives(pdat, "score.compreensao.pos", "grupo", covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- ds
Computing
ANCOVA and PairWise After removing non-normal data (OK)
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo")], wdat[,c("id","grupo")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo"]] = wdat
(non.normal)
## [1] "P270"
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo)
laov[["grupo"]] <- merge(get_anova_table(aov), laov[["grupo"]],
by="Effect", suffixes = c("","'"))
(df = get_anova_table(aov))
## ANOVA Table (type II tests)
##
## Effect DFn DFd F p p<.05 ges
## 1 score.compreensao.pre 1 78 10.330 0.002 * 0.117
## 2 grupo 1 78 0.756 0.387 0.010
| score.compreensao.pre |
1 |
78 |
10.330 |
0.002 |
* |
0.117 |
| grupo |
1 |
78 |
0.756 |
0.387 |
|
0.010 |
pwc <- emmeans_test(wdat, score.compreensao.pos ~ grupo, covariate = score.compreensao.pre,
p.adjust.method = "bonferroni")
| score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
78 |
0.869 |
0.387 |
0.387 |
ns |
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, "grupo"),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo"]] <- merge(plyr::rbind.fill(pwc, pwc.long), lpwc[["grupo"]],
by=c("grupo","term",".y.","group1","group2"),
suffixes = c("","'"))
| Controle |
time |
score.compreensao |
pre |
pos |
158 |
-0.391 |
0.696 |
0.696 |
ns |
| Experimental |
time |
score.compreensao |
pre |
pos |
158 |
-0.365 |
0.716 |
0.716 |
ns |
ds <- get.descriptives(wdat, "score.compreensao.pos", "grupo", covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = "grupo", all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwc), ds, by = "grupo", suffixes = c(".emms", ""))
ds <- ds[,c("grupo","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo"]] <- merge(ds, lemms[["grupo"]], by=c("grupo"), suffixes = c("","'"))
| Controle |
45 |
3.378 |
0.228 |
3.511 |
0.267 |
3.433 |
0.234 |
2.966 |
3.900 |
| Experimental |
36 |
2.889 |
0.272 |
3.028 |
0.244 |
3.125 |
0.263 |
2.603 |
3.648 |
Plots for ancova
plots <- oneWayAncovaPlots(
wdat, "score.compreensao.pos", "grupo", aov, list("grupo"=pwc), addParam = c("mean_ci"),
font.label.size=10, step.increase=0.05, p.label="p.adj",
subtitle = which(aov$Effect == "grupo"))
if (!is.null(nrow(plots[["grupo"]]$data)))
plots[["grupo"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)

plots <- oneWayAncovaBoxPlots(
wdat, "score.compreensao.pos", "grupo", aov, pwc, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo"]],
subtitle = which(aov$Effect == "grupo"))
if (length(unique(wdat[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

if (length(unique(wdat.long[["grupo"]])) > 1)
plots <- oneWayAncovaBoxPlots(
wdat.long, "score.compreensao", "grupo", aov, pwc.long,
pre.post = "time", theme = "classic", color = color$prepost)
if (length(unique(wdat.long[["grupo"]])) > 1)
plots[["grupo"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking normality and
homogeneity
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo, data = wdat))
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.980 0.237
levene_test(res, .resid ~ grupo)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 1 79 1.04 0.310
ANCOVA and
Pairwise for two factors grupo:genero
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["genero"]]),],
"score.compreensao.pos", c("grupo","genero"))
pdat = pdat[pdat[["genero"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["genero"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["genero"]] = factor(
pdat[["genero"]],
level[["genero"]][level[["genero"]] %in% unique(pdat[["genero"]])])
pdat.long <- rbind(pdat[,c("id","grupo","genero")], pdat[,c("id","grupo","genero")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*genero)
laov[["grupo:genero"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ genero,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, genero), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","genero")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","genero"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["genero"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*genero, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","genero")], wdat[,c("id","grupo","genero")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:genero"]] = wdat
(non.normal)
}
## [1] "P270"
if (length(unique(pdat[["genero"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*genero)
laov[["grupo:genero"]] <- merge(get_anova_table(aov), laov[["grupo:genero"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
76 |
10.785 |
0.002 |
* |
0.124 |
| grupo |
1 |
76 |
0.549 |
0.461 |
|
0.007 |
| genero |
1 |
76 |
10.703 |
0.002 |
* |
0.123 |
| grupo:genero |
1 |
76 |
1.524 |
0.221 |
|
0.020 |
if (length(unique(pdat[["genero"]])) >= 2) {
pwcs <- list()
pwcs[["genero"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ genero,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, genero), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["genero"]])
pwc <- pwc[,c("grupo","genero", colnames(pwc)[!colnames(pwc) %in% c("grupo","genero")])]
}
|
F |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
76 |
1.396 |
0.167 |
0.167 |
ns |
|
M |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
76 |
-0.337 |
0.737 |
0.737 |
ns |
| Controle |
|
score.compreensao.pre*genero |
score.compreensao.pos |
F |
M |
76 |
3.261 |
0.002 |
0.002 |
** |
| Experimental |
|
score.compreensao.pre*genero |
score.compreensao.pos |
F |
M |
76 |
1.263 |
0.210 |
0.210 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","genero")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:genero"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:genero"]],
by=c("grupo","genero","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
F |
time |
score.compreensao |
pre |
pos |
154 |
-1.646 |
0.102 |
0.102 |
ns |
| Controle |
M |
time |
score.compreensao |
pre |
pos |
154 |
1.173 |
0.243 |
0.243 |
ns |
| Experimental |
F |
time |
score.compreensao |
pre |
pos |
154 |
-0.869 |
0.386 |
0.386 |
ns |
| Experimental |
M |
time |
score.compreensao |
pre |
pos |
154 |
0.308 |
0.758 |
0.758 |
ns |
if (length(unique(pdat[["genero"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","genero"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","genero"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","genero"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","genero","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","genero", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:genero"]] <- merge(ds, lemms[["grupo:genero"]],
by=c("grupo","genero"), suffixes = c("","'"))
}
| Controle |
F |
24 |
3.458 |
0.356 |
4.208 |
0.366 |
4.105 |
0.302 |
3.504 |
4.707 |
| Controle |
M |
21 |
3.286 |
0.277 |
2.714 |
0.317 |
2.671 |
0.321 |
2.031 |
3.311 |
| Experimental |
F |
17 |
2.882 |
0.382 |
3.353 |
0.296 |
3.449 |
0.358 |
2.736 |
4.162 |
| Experimental |
M |
19 |
2.895 |
0.397 |
2.737 |
0.373 |
2.829 |
0.339 |
2.154 |
3.503 |
Plots for ancova
if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "genero", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
ggPlotAoC2(pwcs, "genero", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:genero"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","genero"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:genero"]],
subtitle = which(aov$Effect == "grupo:genero"))
}
if (length(unique(pdat[["genero"]])) >= 2) {
plots[["grupo:genero"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["genero"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","genero"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["genero"]])) >= 2)
plots[["grupo:genero"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","genero"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "genero", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["genero"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "genero", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = genero)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:genero"))) +
ggplot2::scale_color_manual(values = color[["genero"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["genero"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*genero, data = wdat))
if (length(unique(pdat[["genero"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.981 0.275
if (length(unique(pdat[["genero"]])) >= 2)
levene_test(res, .resid ~ grupo*genero)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 77 1.64 0.187
ANCOVA
and Pairwise for two factors
grupo:zona.participante
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.participante"]]),],
"score.compreensao.pos", c("grupo","zona.participante"))
pdat = pdat[pdat[["zona.participante"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.participante"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.participante"]] = factor(
pdat[["zona.participante"]],
level[["zona.participante"]][level[["zona.participante"]] %in% unique(pdat[["zona.participante"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.participante")], pdat[,c("id","grupo","zona.participante")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ zona.participante,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.participante), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.participante")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","zona.participante"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.participante"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.participante")], wdat[,c("id","grupo","zona.participante")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:zona.participante"]] = wdat
(non.normal)
}
## [1] "P270"
if (length(unique(pdat[["zona.participante"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante)
laov[["grupo:zona.participante"]] <- merge(get_anova_table(aov), laov[["grupo:zona.participante"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
56 |
10.219 |
0.002 |
* |
0.154 |
| grupo |
1 |
56 |
1.373 |
0.246 |
|
0.024 |
| zona.participante |
1 |
56 |
0.341 |
0.561 |
|
0.006 |
| grupo:zona.participante |
1 |
56 |
0.001 |
0.970 |
|
0.000 |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwcs <- list()
pwcs[["zona.participante"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ zona.participante,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.participante), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.participante"]])
pwc <- pwc[,c("grupo","zona.participante", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.participante")])]
}
|
Rural |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
56 |
0.841 |
0.404 |
0.404 |
ns |
|
Urbana |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
56 |
0.821 |
0.415 |
0.415 |
ns |
| Controle |
|
score.compreensao.pre*zona.participante |
score.compreensao.pos |
Rural |
Urbana |
56 |
-0.410 |
0.683 |
0.683 |
ns |
| Experimental |
|
score.compreensao.pre*zona.participante |
score.compreensao.pos |
Rural |
Urbana |
56 |
-0.411 |
0.683 |
0.683 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.participante")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.participante"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.participante"]],
by=c("grupo","zona.participante","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.compreensao |
pre |
pos |
114 |
-0.380 |
0.705 |
0.705 |
ns |
| Controle |
Urbana |
time |
score.compreensao |
pre |
pos |
114 |
-0.641 |
0.523 |
0.523 |
ns |
| Experimental |
Rural |
time |
score.compreensao |
pre |
pos |
114 |
0.352 |
0.726 |
0.726 |
ns |
| Experimental |
Urbana |
time |
score.compreensao |
pre |
pos |
114 |
-1.140 |
0.257 |
0.257 |
ns |
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","zona.participante"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.participante"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.participante"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.participante","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.participante", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.participante"]] <- merge(ds, lemms[["grupo:zona.participante"]],
by=c("grupo","zona.participante"), suffixes = c("","'"))
}
| Controle |
Rural |
12 |
3.417 |
0.260 |
3.667 |
0.284 |
3.605 |
0.437 |
2.730 |
4.480 |
| Controle |
Urbana |
23 |
3.696 |
0.364 |
4.000 |
0.431 |
3.826 |
0.320 |
3.185 |
4.467 |
| Experimental |
Rural |
14 |
3.357 |
0.498 |
3.143 |
0.361 |
3.105 |
0.404 |
2.295 |
3.914 |
| Experimental |
Urbana |
12 |
2.167 |
0.423 |
2.917 |
0.417 |
3.357 |
0.458 |
2.440 |
4.273 |
Plots for ancova
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.participante", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.participante", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.participante"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","zona.participante"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:zona.participante"]],
subtitle = which(aov$Effect == "grupo:zona.participante"))
}
if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots[["grupo:zona.participante"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.participante"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","zona.participante"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.participante"]])) >= 2)
plots[["grupo:zona.participante"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","zona.participante"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "zona.participante", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.participante"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "zona.participante", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.participante)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.participante"))) +
ggplot2::scale_color_manual(values = color[["zona.participante"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.participante"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.participante, data = wdat))
if (length(unique(pdat[["zona.participante"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.970 0.145
if (length(unique(pdat[["zona.participante"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.participante)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 57 2.14 0.105
ANCOVA and
Pairwise for two factors grupo:zona.escola
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["zona.escola"]]),],
"score.compreensao.pos", c("grupo","zona.escola"))
pdat = pdat[pdat[["zona.escola"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["zona.escola"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["zona.escola"]] = factor(
pdat[["zona.escola"]],
level[["zona.escola"]][level[["zona.escola"]] %in% unique(pdat[["zona.escola"]])])
pdat.long <- rbind(pdat[,c("id","grupo","zona.escola")], pdat[,c("id","grupo","zona.escola")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ zona.escola,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, zona.escola), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","zona.escola")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","zona.escola"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["zona.escola"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","zona.escola")], wdat[,c("id","grupo","zona.escola")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:zona.escola"]] = wdat
(non.normal)
}
## [1] "P270"
if (length(unique(pdat[["zona.escola"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola)
laov[["grupo:zona.escola"]] <- merge(get_anova_table(aov), laov[["grupo:zona.escola"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
76 |
9.031 |
0.004 |
* |
0.106 |
| grupo |
1 |
76 |
0.879 |
0.351 |
|
0.011 |
| zona.escola |
1 |
76 |
1.061 |
0.306 |
|
0.014 |
| grupo:zona.escola |
1 |
76 |
0.105 |
0.747 |
|
0.001 |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwcs <- list()
pwcs[["zona.escola"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ zona.escola,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, zona.escola), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["zona.escola"]])
pwc <- pwc[,c("grupo","zona.escola", colnames(pwc)[!colnames(pwc) %in% c("grupo","zona.escola")])]
}
|
Rural |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
76 |
0.254 |
0.800 |
0.800 |
ns |
|
Urbana |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
76 |
0.962 |
0.339 |
0.339 |
ns |
| Controle |
|
score.compreensao.pre*zona.escola |
score.compreensao.pos |
Rural |
Urbana |
76 |
0.528 |
0.599 |
0.599 |
ns |
| Experimental |
|
score.compreensao.pre*zona.escola |
score.compreensao.pos |
Rural |
Urbana |
76 |
0.945 |
0.348 |
0.348 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","zona.escola")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:zona.escola"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:zona.escola"]],
by=c("grupo","zona.escola","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
Rural |
time |
score.compreensao |
pre |
pos |
154 |
0.122 |
0.903 |
0.903 |
ns |
| Controle |
Urbana |
time |
score.compreensao |
pre |
pos |
154 |
-0.543 |
0.588 |
0.588 |
ns |
| Experimental |
Rural |
time |
score.compreensao |
pre |
pos |
154 |
-0.634 |
0.527 |
0.527 |
ns |
| Experimental |
Urbana |
time |
score.compreensao |
pre |
pos |
154 |
0.000 |
1.000 |
1.000 |
ns |
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","zona.escola"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","zona.escola"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","zona.escola"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","zona.escola","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","zona.escola", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:zona.escola"]] <- merge(ds, lemms[["grupo:zona.escola"]],
by=c("grupo","zona.escola"), suffixes = c("","'"))
}
| Controle |
Rural |
13 |
4.000 |
0.506 |
3.923 |
0.445 |
3.634 |
0.447 |
2.745 |
4.524 |
| Controle |
Urbana |
32 |
3.125 |
0.237 |
3.344 |
0.329 |
3.356 |
0.278 |
2.802 |
3.910 |
| Experimental |
Rural |
12 |
3.000 |
0.408 |
3.417 |
0.398 |
3.472 |
0.454 |
2.567 |
4.377 |
| Experimental |
Urbana |
24 |
2.833 |
0.359 |
2.833 |
0.305 |
2.946 |
0.323 |
2.302 |
3.590 |
Plots for ancova
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "zona.escola", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggPlotAoC2(pwcs, "zona.escola", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:zona.escola"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","zona.escola"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:zona.escola"]],
subtitle = which(aov$Effect == "grupo:zona.escola"))
}
if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots[["grupo:zona.escola"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["zona.escola"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","zona.escola"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["zona.escola"]])) >= 2)
plots[["grupo:zona.escola"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","zona.escola"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "zona.escola", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["zona.escola"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "zona.escola", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = zona.escola)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:zona.escola"))) +
ggplot2::scale_color_manual(values = color[["zona.escola"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["zona.escola"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*zona.escola, data = wdat))
if (length(unique(pdat[["zona.escola"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.984 0.398
if (length(unique(pdat[["zona.escola"]])) >= 2)
levene_test(res, .resid ~ grupo*zona.escola)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 77 0.515 0.673
ANCOVA
and Pairwise for two factors
grupo:score.compreensao.quintile
Without remove non-normal
data
pdat = remove_group_data(dat[!is.na(dat[["grupo"]]) & !is.na(dat[["score.compreensao.quintile"]]),],
"score.compreensao.pos", c("grupo","score.compreensao.quintile"))
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning:
## ! There was 1 warning in `mutate()`.
## ℹ In argument: `ci = abs(stats::qt(alpha/2, .data$n - 1) * .data$se)`.
## Caused by warning in `stats::qt()`:
## ! NaNs produced
pdat = pdat[pdat[["score.compreensao.quintile"]] %in% do.call(
intersect, lapply(unique(pdat[["grupo"]]), FUN = function(x) {
unique(pdat[["score.compreensao.quintile"]][which(pdat[["grupo"]] == x)])
})),]
pdat[["grupo"]] = factor(pdat[["grupo"]], level[["grupo"]])
pdat[["score.compreensao.quintile"]] = factor(
pdat[["score.compreensao.quintile"]],
level[["score.compreensao.quintile"]][level[["score.compreensao.quintile"]] %in% unique(pdat[["score.compreensao.quintile"]])])
pdat.long <- rbind(pdat[,c("id","grupo","score.compreensao.quintile")], pdat[,c("id","grupo","score.compreensao.quintile")])
pdat.long[["time"]] <- c(rep("pre", nrow(pdat)), rep("pos", nrow(pdat)))
pdat.long[["time"]] <- factor(pdat.long[["time"]], c("pre","pos"))
pdat.long[["score.compreensao"]] <- c(pdat[["score.compreensao.pre"]], pdat[["score.compreensao.pos"]])
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
aov = anova_test(pdat, score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile)
laov[["grupo:score.compreensao.quintile"]] <- get_anova_table(aov)
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.compreensao.quintile"]] <- emmeans_test(
group_by(pdat, grupo), score.compreensao.pos ~ score.compreensao.quintile,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(pdat, score.compreensao.quintile), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.compreensao.quintile"]])
pwc <- pwc[,c("grupo","score.compreensao.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.compreensao.quintile")])]
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(pdat.long, c("grupo","score.compreensao.quintile")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.compreensao.quintile"]] <- plyr::rbind.fill(pwc, pwc.long)
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ds <- get.descriptives(pdat, "score.compreensao.pos", c("grupo","score.compreensao.quintile"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.compreensao.quintile"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.compreensao.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.compreensao.quintile","n","mean.score.compreensao.pre","se.score.compreensao.pre","mean","se",
"emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.compreensao.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.compreensao.quintile"]] <- ds
}
Computing
ANCOVA and PairWise After removing non-normal data (OK)
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
wdat = pdat
res = residuals(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile, data = wdat))
non.normal = getNonNormal(res, wdat$id, plimit = 0.05)
wdat = wdat[!wdat$id %in% non.normal,]
wdat.long <- rbind(wdat[,c("id","grupo","score.compreensao.quintile")], wdat[,c("id","grupo","score.compreensao.quintile")])
wdat.long[["time"]] <- c(rep("pre", nrow(wdat)), rep("pos", nrow(wdat)))
wdat.long[["time"]] <- factor(wdat.long[["time"]], c("pre","pos"))
wdat.long[["score.compreensao"]] <- c(wdat[["score.compreensao.pre"]], wdat[["score.compreensao.pos"]])
ldat[["grupo:score.compreensao.quintile"]] = wdat
(non.normal)
}
## [1] "P270"
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
aov = anova_test(wdat, score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile)
laov[["grupo:score.compreensao.quintile"]] <- merge(get_anova_table(aov), laov[["grupo:score.compreensao.quintile"]],
by="Effect", suffixes = c("","'"))
df = get_anova_table(aov)
}
| score.compreensao.pre |
1 |
66 |
0.197 |
0.658 |
|
0.003 |
| grupo |
1 |
66 |
0.173 |
0.679 |
|
0.003 |
| score.compreensao.quintile |
1 |
66 |
0.412 |
0.523 |
|
0.006 |
| grupo:score.compreensao.quintile |
1 |
66 |
0.045 |
0.833 |
|
0.001 |
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwcs <- list()
pwcs[["score.compreensao.quintile"]] <- emmeans_test(
group_by(wdat, grupo), score.compreensao.pos ~ score.compreensao.quintile,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwcs[["grupo"]] <- emmeans_test(
group_by(wdat, score.compreensao.quintile), score.compreensao.pos ~ grupo,
covariate = score.compreensao.pre, p.adjust.method = "bonferroni")
pwc <- plyr::rbind.fill(pwcs[["grupo"]], pwcs[["score.compreensao.quintile"]])
pwc <- pwc[,c("grupo","score.compreensao.quintile", colnames(pwc)[!colnames(pwc) %in% c("grupo","score.compreensao.quintile")])]
}
|
2nd quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
66 |
0.210 |
0.834 |
0.834 |
ns |
|
3rd quintile |
score.compreensao.pre*grupo |
score.compreensao.pos |
Controle |
Experimental |
66 |
0.406 |
0.686 |
0.686 |
ns |
| Controle |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
3rd quintile |
66 |
-0.647 |
0.520 |
0.520 |
ns |
| Experimental |
|
score.compreensao.pre*score.compreensao.quintile |
score.compreensao.pos |
2nd quintile |
3rd quintile |
66 |
-0.292 |
0.771 |
0.771 |
ns |
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
pwc.long <- emmeans_test(dplyr::group_by_at(wdat.long, c("grupo","score.compreensao.quintile")),
score.compreensao ~ time,
p.adjust.method = "bonferroni")
lpwc[["grupo:score.compreensao.quintile"]] <- merge(plyr::rbind.fill(pwc, pwc.long),
lpwc[["grupo:score.compreensao.quintile"]],
by=c("grupo","score.compreensao.quintile","term",".y.","group1","group2"),
suffixes = c("","'"))
}
| Controle |
2nd quintile |
time |
score.compreensao |
pre |
pos |
134 |
-1.320 |
0.189 |
0.189 |
ns |
| Controle |
3rd quintile |
time |
score.compreensao |
pre |
pos |
134 |
1.356 |
0.177 |
0.177 |
ns |
| Experimental |
2nd quintile |
time |
score.compreensao |
pre |
pos |
134 |
-1.381 |
0.170 |
0.170 |
ns |
| Experimental |
3rd quintile |
time |
score.compreensao |
pre |
pos |
134 |
2.685 |
0.008 |
0.008 |
** |
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ds <- get.descriptives(wdat, "score.compreensao.pos", c("grupo","score.compreensao.quintile"), covar = "score.compreensao.pre")
ds <- merge(ds[ds$variable != "score.compreensao.pre",],
ds[ds$variable == "score.compreensao.pre", !colnames(ds) %in% c("variable")],
by = c("grupo","score.compreensao.quintile"), all.x = T, suffixes = c("", ".score.compreensao.pre"))
ds <- merge(get_emmeans(pwcs[["grupo"]]), ds,
by = c("grupo","score.compreensao.quintile"), suffixes = c(".emms", ""))
ds <- ds[,c("grupo","score.compreensao.quintile","n","mean.score.compreensao.pre","se.score.compreensao.pre",
"mean","se","emmean","se.emms","conf.low","conf.high")]
colnames(ds) <- c("grupo","score.compreensao.quintile", "N", paste0(c("M","SE")," (pre)"),
paste0(c("M","SE"), " (unadj)"),
paste0(c("M", "SE"), " (adj)"), "conf.low", "conf.high")
lemms[["grupo:score.compreensao.quintile"]] <- merge(ds, lemms[["grupo:score.compreensao.quintile"]],
by=c("grupo","score.compreensao.quintile"), suffixes = c("","'"))
}
| Controle |
2nd quintile |
23 |
2.609 |
0.104 |
3.087 |
0.350 |
3.210 |
0.443 |
2.326 |
4.095 |
| Controle |
3rd quintile |
18 |
4.500 |
0.146 |
3.944 |
0.431 |
3.765 |
0.561 |
2.645 |
4.885 |
| Experimental |
2nd quintile |
21 |
2.429 |
0.111 |
2.952 |
0.320 |
3.105 |
0.498 |
2.111 |
4.099 |
| Experimental |
3rd quintile |
9 |
5.333 |
0.236 |
3.778 |
0.521 |
3.465 |
0.894 |
1.681 |
5.250 |
Plots for ancova
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "grupo", "score.compreensao.quintile", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:score.compreensao.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["score.compreensao.quintile"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggPlotAoC2(pwcs, "score.compreensao.quintile", "grupo", aov, ylab = "Reading Comprehension",
subtitle = which(aov$Effect == "grupo:score.compreensao.quintile"), addParam = "errorbar") +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin.ci < ymax.ci) ggplot2::ylim(ymin.ci, ymax.ci)
}
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat, "score.compreensao.pos", c("grupo","score.compreensao.quintile"), aov, pwcs, covar = "score.compreensao.pre",
theme = "classic", color = color[["grupo:score.compreensao.quintile"]],
subtitle = which(aov$Effect == "grupo:score.compreensao.quintile"))
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
plots[["grupo:score.compreensao.quintile"]] + ggplot2::ylab("Reading Comprehension") +
ggplot2::scale_x_discrete(labels=c('pre', 'pos')) +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}
## Warning: No shared levels found between `names(values)` of the manual scale and the data's colour values.

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
plots <- twoWayAncovaBoxPlots(
wdat.long, "score.compreensao", c("grupo","score.compreensao.quintile"), aov, pwc.long,
pre.post = "time",
theme = "classic", color = color$prepost)
}
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
plots[["grupo:score.compreensao.quintile"]] + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)

Checking linearity
assumption
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
facet.by = c("grupo","score.compreensao.quintile"), add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"))
) + ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "grupo", facet.by = "score.compreensao.quintile", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = grupo)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.compreensao.quintile"))) +
ggplot2::scale_color_manual(values = color[["grupo"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2) {
ggscatter(wdat, x = "score.compreensao.pre", y = "score.compreensao.pos", size = 0.5,
color = "score.compreensao.quintile", facet.by = "grupo", add = "reg.line")+
stat_regline_equation(
aes(label = paste(..eq.label.., ..rr.label.., sep = "~~~~"), color = score.compreensao.quintile)
) +
ggplot2::labs(subtitle = rstatix::get_test_label(aov, detailed = T, row = which(aov$Effect == "grupo:score.compreensao.quintile"))) +
ggplot2::scale_color_manual(values = color[["score.compreensao.quintile"]]) +
ggplot2::ylab("Reading Comprehension") +
if (ymin < ymax) ggplot2::ylim(ymin, ymax)
}

Checking normality and
homogeneity
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
res <- augment(lm(score.compreensao.pos ~ score.compreensao.pre + grupo*score.compreensao.quintile, data = wdat))
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
shapiro_test(res$.resid)
## # A tibble: 1 × 3
## variable statistic p.value
## <chr> <dbl> <dbl>
## 1 res$.resid 0.968 0.0702
if (length(unique(pdat[["score.compreensao.quintile"]])) >= 2)
levene_test(res, .resid ~ grupo*score.compreensao.quintile)
## # A tibble: 1 × 4
## df1 df2 statistic p
## <int> <int> <dbl> <dbl>
## 1 3 67 0.141 0.935
Summary of Results
Descriptive Statistics
df <- get.descriptives(ldat[["grupo"]], c(dv.pre, dv.pos), c("grupo"),
include.global = T, symmetry.test = T, normality.test = F)
df <- plyr::rbind.fill(
df, do.call(plyr::rbind.fill, lapply(lfatores2, FUN = function(f) {
if (nrow(dat) > 0 && sum(!is.na(unique(dat[[f]]))) > 1 && paste0("grupo:",f) %in% names(ldat))
get.descriptives(ldat[[paste0("grupo:",f)]], c(dv.pre,dv.pos), c("grupo", f),
symmetry.test = T, normality.test = F)
}))
)
df <- df[,c(fatores1[fatores1 %in% colnames(df)],"variable",
colnames(df)[!colnames(df) %in% c(fatores1,"variable")])]
| Controle |
|
|
|
|
score.compreensao.pre |
45 |
3.378 |
3.0 |
0 |
9 |
1.527 |
0.228 |
0.459 |
2.00 |
NO |
0.864 |
2.410 |
| Experimental |
|
|
|
|
score.compreensao.pre |
36 |
2.889 |
2.5 |
0 |
6 |
1.635 |
0.272 |
0.553 |
1.25 |
NO |
0.593 |
-0.681 |
|
|
|
|
|
score.compreensao.pre |
81 |
3.160 |
3.0 |
0 |
9 |
1.585 |
0.176 |
0.350 |
2.00 |
NO |
0.688 |
0.933 |
| Controle |
|
|
|
|
score.compreensao.pos |
45 |
3.511 |
3.0 |
0 |
9 |
1.792 |
0.267 |
0.538 |
2.00 |
NO |
0.573 |
0.436 |
| Experimental |
|
|
|
|
score.compreensao.pos |
36 |
3.028 |
3.0 |
1 |
6 |
1.464 |
0.244 |
0.495 |
2.00 |
YES |
0.219 |
-0.894 |
|
|
|
|
|
score.compreensao.pos |
81 |
3.296 |
3.0 |
0 |
9 |
1.662 |
0.185 |
0.367 |
2.00 |
NO |
0.548 |
0.423 |
| Controle |
F |
|
|
|
score.compreensao.pre |
24 |
3.458 |
3.0 |
0 |
9 |
1.744 |
0.356 |
0.736 |
1.25 |
NO |
0.915 |
2.243 |
| Controle |
M |
|
|
|
score.compreensao.pre |
21 |
3.286 |
3.0 |
1 |
6 |
1.271 |
0.277 |
0.578 |
2.00 |
YES |
0.321 |
-0.766 |
| Experimental |
F |
|
|
|
score.compreensao.pre |
17 |
2.882 |
2.0 |
1 |
6 |
1.576 |
0.382 |
0.811 |
1.00 |
NO |
0.992 |
-0.640 |
| Experimental |
M |
|
|
|
score.compreensao.pre |
19 |
2.895 |
3.0 |
0 |
6 |
1.729 |
0.397 |
0.833 |
2.00 |
YES |
0.275 |
-0.938 |
| Controle |
F |
|
|
|
score.compreensao.pos |
24 |
4.208 |
4.0 |
2 |
9 |
1.793 |
0.366 |
0.757 |
2.00 |
NO |
0.654 |
0.015 |
| Controle |
M |
|
|
|
score.compreensao.pos |
21 |
2.714 |
3.0 |
0 |
6 |
1.454 |
0.317 |
0.662 |
2.00 |
YES |
0.104 |
-0.626 |
| Experimental |
F |
|
|
|
score.compreensao.pos |
17 |
3.353 |
3.0 |
1 |
6 |
1.222 |
0.296 |
0.628 |
1.00 |
YES |
0.321 |
-0.312 |
| Experimental |
M |
|
|
|
score.compreensao.pos |
19 |
2.737 |
2.0 |
1 |
6 |
1.628 |
0.373 |
0.784 |
3.00 |
YES |
0.403 |
-1.261 |
| Controle |
|
Rural |
|
|
score.compreensao.pre |
12 |
3.417 |
3.5 |
2 |
5 |
0.900 |
0.260 |
0.572 |
1.00 |
YES |
-0.116 |
-1.093 |
| Controle |
|
Urbana |
|
|
score.compreensao.pre |
23 |
3.696 |
3.0 |
1 |
9 |
1.743 |
0.364 |
0.754 |
2.50 |
NO |
1.040 |
1.376 |
| Experimental |
|
Rural |
|
|
score.compreensao.pre |
14 |
3.357 |
3.0 |
1 |
6 |
1.865 |
0.498 |
1.077 |
3.00 |
YES |
0.307 |
-1.605 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pre |
12 |
2.167 |
2.0 |
0 |
6 |
1.467 |
0.423 |
0.932 |
0.50 |
NO |
1.165 |
1.368 |
| Controle |
|
Rural |
|
|
score.compreensao.pos |
12 |
3.667 |
4.0 |
2 |
5 |
0.985 |
0.284 |
0.626 |
1.00 |
YES |
-0.427 |
-1.031 |
| Controle |
|
Urbana |
|
|
score.compreensao.pos |
23 |
4.000 |
4.0 |
1 |
9 |
2.067 |
0.431 |
0.894 |
3.50 |
YES |
0.443 |
-0.576 |
| Experimental |
|
Rural |
|
|
score.compreensao.pos |
14 |
3.143 |
3.0 |
1 |
6 |
1.351 |
0.361 |
0.780 |
2.00 |
YES |
0.458 |
-0.680 |
| Experimental |
|
Urbana |
|
|
score.compreensao.pos |
12 |
2.917 |
3.0 |
1 |
5 |
1.443 |
0.417 |
0.917 |
2.25 |
YES |
-0.035 |
-1.433 |
| Controle |
|
|
Rural |
|
score.compreensao.pre |
13 |
4.000 |
4.0 |
1 |
9 |
1.826 |
0.506 |
1.103 |
1.00 |
NO |
1.213 |
1.929 |
| Controle |
|
|
Urbana |
|
score.compreensao.pre |
32 |
3.125 |
3.0 |
0 |
6 |
1.338 |
0.237 |
0.482 |
2.00 |
YES |
0.093 |
-0.434 |
| Experimental |
|
|
Rural |
|
score.compreensao.pre |
12 |
3.000 |
3.0 |
1 |
6 |
1.414 |
0.408 |
0.899 |
1.25 |
NO |
0.707 |
-0.542 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pre |
24 |
2.833 |
2.0 |
0 |
6 |
1.761 |
0.359 |
0.744 |
1.50 |
NO |
0.563 |
-0.915 |
| Controle |
|
|
Rural |
|
score.compreensao.pos |
13 |
3.923 |
4.0 |
1 |
7 |
1.605 |
0.445 |
0.970 |
2.00 |
YES |
0.114 |
-0.686 |
| Controle |
|
|
Urbana |
|
score.compreensao.pos |
32 |
3.344 |
3.0 |
0 |
9 |
1.860 |
0.329 |
0.671 |
2.00 |
NO |
0.763 |
0.758 |
| Experimental |
|
|
Rural |
|
score.compreensao.pos |
12 |
3.417 |
3.5 |
1 |
6 |
1.379 |
0.398 |
0.876 |
1.25 |
YES |
0.063 |
-0.844 |
| Experimental |
|
|
Urbana |
|
score.compreensao.pos |
24 |
2.833 |
3.0 |
1 |
6 |
1.494 |
0.305 |
0.631 |
2.25 |
YES |
0.347 |
-0.976 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pre |
23 |
2.609 |
3.0 |
2 |
3 |
0.499 |
0.104 |
0.216 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pre |
18 |
4.500 |
4.0 |
4 |
6 |
0.618 |
0.146 |
0.307 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pre |
21 |
2.429 |
2.0 |
2 |
3 |
0.507 |
0.111 |
0.231 |
1.00 |
few data |
0.000 |
0.000 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pre |
9 |
5.333 |
5.0 |
4 |
6 |
0.707 |
0.236 |
0.544 |
1.00 |
few data |
0.000 |
0.000 |
| Controle |
|
|
|
2nd quintile |
score.compreensao.pos |
23 |
3.087 |
3.0 |
0 |
6 |
1.676 |
0.350 |
0.725 |
2.00 |
YES |
0.202 |
-0.866 |
| Controle |
|
|
|
3rd quintile |
score.compreensao.pos |
18 |
3.944 |
4.0 |
1 |
9 |
1.830 |
0.431 |
0.910 |
2.00 |
NO |
0.838 |
0.944 |
| Experimental |
|
|
|
2nd quintile |
score.compreensao.pos |
21 |
2.952 |
3.0 |
1 |
6 |
1.465 |
0.320 |
0.667 |
2.00 |
YES |
0.169 |
-0.930 |
| Experimental |
|
|
|
3rd quintile |
score.compreensao.pos |
9 |
3.778 |
4.0 |
1 |
6 |
1.563 |
0.521 |
1.202 |
2.00 |
YES |
-0.374 |
-1.164 |
ANCOVA Table Comparison
df <- do.call(plyr::rbind.fill, laov)
df <- df[!duplicated(df$Effect),]
| 1 |
grupo |
1 |
78 |
0.756 |
0.387 |
|
0.010 |
1 |
79 |
0.157 |
0.693 |
|
0.002 |
| 2 |
score.compreensao.pre |
1 |
78 |
10.330 |
0.002 |
* |
0.117 |
1 |
79 |
7.451 |
0.008 |
* |
0.086 |
| 3 |
genero |
1 |
76 |
10.703 |
0.002 |
* |
0.123 |
1 |
77 |
11.788 |
0.001 |
* |
0.133 |
| 5 |
grupo:genero |
1 |
76 |
1.524 |
0.221 |
|
0.020 |
1 |
77 |
0.471 |
0.494 |
|
0.006 |
| 8 |
grupo:zona.participante |
1 |
56 |
0.001 |
0.970 |
|
0.000 |
1 |
57 |
0.265 |
0.609 |
|
0.005 |
| 10 |
zona.participante |
1 |
56 |
0.341 |
0.561 |
|
0.006 |
1 |
57 |
0.003 |
0.955 |
|
0.000 |
| 12 |
grupo:zona.escola |
1 |
76 |
0.105 |
0.747 |
|
0.001 |
1 |
77 |
0.001 |
0.972 |
|
0.000 |
| 14 |
zona.escola |
1 |
76 |
1.061 |
0.306 |
|
0.014 |
1 |
77 |
0.474 |
0.493 |
|
0.006 |
| 16 |
grupo:score.compreensao.quintile |
1 |
66 |
0.045 |
0.833 |
|
0.001 |
1 |
67 |
0.125 |
0.725 |
|
0.002 |
| 18 |
score.compreensao.quintile |
1 |
66 |
0.412 |
0.523 |
|
0.006 |
1 |
67 |
0.687 |
0.410 |
|
0.010 |
PairWise Table Comparison
df <- do.call(plyr::rbind.fill, lpwc)
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% c(names(lfatores),"term",".y.")])]
| Controle |
|
|
|
|
pre |
pos |
158 |
-0.391 |
0.696 |
0.696 |
ns |
160 |
-0.378 |
0.706 |
0.706 |
ns |
| Experimental |
|
|
|
|
pre |
pos |
158 |
-0.365 |
0.716 |
0.716 |
ns |
160 |
-0.834 |
0.406 |
0.406 |
ns |
|
|
|
|
|
Controle |
Experimental |
78 |
0.869 |
0.387 |
0.387 |
ns |
79 |
0.397 |
0.693 |
0.693 |
ns |
| Controle |
F |
|
|
|
pre |
pos |
154 |
-1.646 |
0.102 |
0.102 |
ns |
156 |
-1.594 |
0.113 |
0.113 |
ns |
| Controle |
M |
|
|
|
pre |
pos |
154 |
1.173 |
0.243 |
0.243 |
ns |
156 |
1.136 |
0.258 |
0.258 |
ns |
| Controle |
|
|
|
|
F |
M |
76 |
3.261 |
0.002 |
0.002 |
** |
77 |
3.002 |
0.004 |
0.004 |
** |
| Experimental |
F |
|
|
|
pre |
pos |
154 |
-0.869 |
0.386 |
0.386 |
ns |
156 |
-1.534 |
0.127 |
0.127 |
ns |
| Experimental |
M |
|
|
|
pre |
pos |
154 |
0.308 |
0.758 |
0.758 |
ns |
156 |
0.299 |
0.766 |
0.766 |
ns |
| Experimental |
|
|
|
|
F |
M |
76 |
1.263 |
0.210 |
0.210 |
ns |
77 |
1.801 |
0.076 |
0.076 |
ns |
|
F |
|
|
|
Controle |
Experimental |
76 |
1.396 |
0.167 |
0.167 |
ns |
77 |
0.677 |
0.501 |
0.501 |
ns |
|
M |
|
|
|
Controle |
Experimental |
76 |
-0.337 |
0.737 |
0.737 |
ns |
77 |
-0.290 |
0.773 |
0.773 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
56 |
-0.410 |
0.683 |
0.683 |
ns |
57 |
-0.389 |
0.699 |
0.699 |
ns |
| Controle |
|
Rural |
|
|
pre |
pos |
114 |
-0.380 |
0.705 |
0.705 |
ns |
116 |
-0.363 |
0.717 |
0.717 |
ns |
| Controle |
|
Urbana |
|
|
pre |
pos |
114 |
-0.641 |
0.523 |
0.523 |
ns |
116 |
-0.612 |
0.542 |
0.542 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
56 |
-0.411 |
0.683 |
0.683 |
ns |
57 |
0.347 |
0.730 |
0.730 |
ns |
| Experimental |
|
Rural |
|
|
pre |
pos |
114 |
0.352 |
0.726 |
0.726 |
ns |
116 |
-0.433 |
0.666 |
0.666 |
ns |
| Experimental |
|
Urbana |
|
|
pre |
pos |
114 |
-1.140 |
0.257 |
0.257 |
ns |
116 |
-1.090 |
0.278 |
0.278 |
ns |
|
|
Rural |
|
|
Controle |
Experimental |
56 |
0.841 |
0.404 |
0.404 |
ns |
57 |
0.123 |
0.902 |
0.902 |
ns |
|
|
Urbana |
|
|
Controle |
Experimental |
56 |
0.821 |
0.415 |
0.415 |
ns |
57 |
0.859 |
0.394 |
0.394 |
ns |
| Controle |
|
|
|
|
Rural |
Urbana |
76 |
0.528 |
0.599 |
0.599 |
ns |
77 |
0.524 |
0.602 |
0.602 |
ns |
| Controle |
|
|
Rural |
|
pre |
pos |
154 |
0.122 |
0.903 |
0.903 |
ns |
156 |
0.117 |
0.907 |
0.907 |
ns |
| Controle |
|
|
Urbana |
|
pre |
pos |
154 |
-0.543 |
0.588 |
0.588 |
ns |
156 |
-0.523 |
0.602 |
0.602 |
ns |
| Experimental |
|
|
|
|
Rural |
Urbana |
76 |
0.945 |
0.348 |
0.348 |
ns |
77 |
0.452 |
0.653 |
0.653 |
ns |
| Experimental |
|
|
Rural |
|
pre |
pos |
154 |
-0.634 |
0.527 |
0.527 |
ns |
156 |
-0.610 |
0.543 |
0.543 |
ns |
| Experimental |
|
|
Urbana |
|
pre |
pos |
154 |
0.000 |
1.000 |
1.000 |
ns |
156 |
-0.592 |
0.555 |
0.555 |
ns |
|
|
|
Rural |
|
Controle |
Experimental |
76 |
0.254 |
0.800 |
0.800 |
ns |
77 |
0.271 |
0.787 |
0.787 |
ns |
|
|
|
Urbana |
|
Controle |
Experimental |
76 |
0.962 |
0.339 |
0.339 |
ns |
77 |
0.349 |
0.728 |
0.728 |
ns |
| Controle |
|
|
|
2nd quintile |
pre |
pos |
134 |
-1.320 |
0.189 |
0.189 |
ns |
136 |
-1.227 |
0.222 |
0.222 |
ns |
| Controle |
|
|
|
3rd quintile |
pre |
pos |
134 |
1.356 |
0.177 |
0.177 |
ns |
136 |
1.261 |
0.209 |
0.209 |
ns |
| Controle |
|
|
|
|
2nd quintile |
3rd quintile |
66 |
-0.647 |
0.520 |
0.520 |
ns |
67 |
-0.837 |
0.405 |
0.405 |
ns |
| Experimental |
|
|
|
2nd quintile |
pre |
pos |
134 |
-1.381 |
0.170 |
0.170 |
ns |
136 |
-2.054 |
0.042 |
0.042 |
* |
| Experimental |
|
|
|
3rd quintile |
pre |
pos |
134 |
2.685 |
0.008 |
0.008 |
** |
136 |
2.497 |
0.014 |
0.014 |
* |
| Experimental |
|
|
|
|
2nd quintile |
3rd quintile |
66 |
-0.292 |
0.771 |
0.771 |
ns |
67 |
-0.318 |
0.752 |
0.752 |
ns |
|
|
|
|
2nd quintile |
Controle |
Experimental |
66 |
0.210 |
0.834 |
0.834 |
ns |
67 |
-0.275 |
0.784 |
0.784 |
ns |
|
|
|
|
3rd quintile |
Controle |
Experimental |
66 |
0.406 |
0.686 |
0.686 |
ns |
67 |
0.252 |
0.802 |
0.802 |
ns |
EMMS Table Comparison
df <- do.call(plyr::rbind.fill, lemms)
df[["N-N'"]] <- df[["N"]] - df[["N'"]]
df <- df[,c(names(lfatores)[names(lfatores) %in% colnames(df)],
names(df)[!names(df) %in% names(lfatores)])]
| Controle |
|
|
|
|
45 |
3.378 |
0.228 |
3.511 |
0.267 |
3.433 |
0.234 |
2.966 |
3.900 |
45 |
3.378 |
0.228 |
3.511 |
0.267 |
3.434 |
0.256 |
2.926 |
3.943 |
0 |
| Experimental |
|
|
|
|
36 |
2.889 |
0.272 |
3.028 |
0.244 |
3.125 |
0.263 |
2.603 |
3.648 |
37 |
2.865 |
0.266 |
3.189 |
0.287 |
3.282 |
0.282 |
2.721 |
3.844 |
-1 |
| Controle |
F |
|
|
|
24 |
3.458 |
0.356 |
4.208 |
0.366 |
4.105 |
0.302 |
3.504 |
4.707 |
24 |
3.458 |
0.356 |
4.208 |
0.366 |
4.108 |
0.329 |
3.453 |
4.764 |
0 |
| Controle |
M |
|
|
|
21 |
3.286 |
0.277 |
2.714 |
0.317 |
2.671 |
0.321 |
2.031 |
3.311 |
21 |
3.286 |
0.277 |
2.714 |
0.317 |
2.670 |
0.350 |
1.973 |
3.367 |
0 |
| Experimental |
F |
|
|
|
17 |
2.882 |
0.382 |
3.353 |
0.296 |
3.449 |
0.358 |
2.736 |
4.162 |
18 |
2.833 |
0.364 |
3.667 |
0.420 |
3.767 |
0.379 |
3.011 |
4.522 |
-1 |
| Experimental |
M |
|
|
|
19 |
2.895 |
0.397 |
2.737 |
0.373 |
2.829 |
0.339 |
2.154 |
3.503 |
19 |
2.895 |
0.397 |
2.737 |
0.373 |
2.817 |
0.369 |
2.083 |
3.552 |
0 |
| Controle |
|
Rural |
|
|
12 |
3.417 |
0.260 |
3.667 |
0.284 |
3.605 |
0.437 |
2.730 |
4.480 |
12 |
3.417 |
0.260 |
3.667 |
0.284 |
3.606 |
0.493 |
2.619 |
4.593 |
0 |
| Controle |
|
Urbana |
|
|
23 |
3.696 |
0.364 |
4.000 |
0.431 |
3.826 |
0.320 |
3.185 |
4.467 |
23 |
3.696 |
0.364 |
4.000 |
0.431 |
3.843 |
0.361 |
3.119 |
4.566 |
0 |
| Experimental |
|
Rural |
|
|
14 |
3.357 |
0.498 |
3.143 |
0.361 |
3.105 |
0.404 |
2.295 |
3.914 |
15 |
3.267 |
0.473 |
3.533 |
0.515 |
3.525 |
0.440 |
2.643 |
4.406 |
-1 |
| Experimental |
|
Urbana |
|
|
12 |
2.167 |
0.423 |
2.917 |
0.417 |
3.357 |
0.458 |
2.440 |
4.273 |
12 |
2.167 |
0.423 |
2.917 |
0.417 |
3.289 |
0.515 |
2.258 |
4.321 |
0 |
| Controle |
|
|
Rural |
|
13 |
4.000 |
0.506 |
3.923 |
0.445 |
3.634 |
0.447 |
2.745 |
4.524 |
13 |
4.000 |
0.506 |
3.923 |
0.445 |
3.653 |
0.489 |
2.679 |
4.626 |
0 |
| Controle |
|
|
Urbana |
|
32 |
3.125 |
0.237 |
3.344 |
0.329 |
3.356 |
0.278 |
2.802 |
3.910 |
32 |
3.125 |
0.237 |
3.344 |
0.329 |
3.351 |
0.304 |
2.745 |
3.956 |
0 |
| Experimental |
|
|
Rural |
|
12 |
3.000 |
0.408 |
3.417 |
0.398 |
3.472 |
0.454 |
2.567 |
4.377 |
12 |
3.000 |
0.408 |
3.417 |
0.398 |
3.463 |
0.497 |
2.473 |
4.453 |
0 |
| Experimental |
|
|
Urbana |
|
24 |
2.833 |
0.359 |
2.833 |
0.305 |
2.946 |
0.323 |
2.302 |
3.590 |
25 |
2.800 |
0.346 |
3.080 |
0.383 |
3.190 |
0.347 |
2.499 |
3.880 |
-1 |
| Controle |
|
|
|
2nd quintile |
23 |
2.609 |
0.104 |
3.087 |
0.350 |
3.210 |
0.443 |
2.326 |
4.095 |
23 |
2.609 |
0.104 |
3.087 |
0.350 |
3.119 |
0.475 |
2.170 |
4.068 |
0 |
| Controle |
|
|
|
3rd quintile |
18 |
4.500 |
0.146 |
3.944 |
0.431 |
3.765 |
0.561 |
2.645 |
4.885 |
18 |
4.500 |
0.146 |
3.944 |
0.431 |
3.896 |
0.613 |
2.673 |
5.120 |
0 |
| Experimental |
|
|
|
2nd quintile |
21 |
2.429 |
0.111 |
2.952 |
0.320 |
3.105 |
0.498 |
2.111 |
4.099 |
22 |
2.409 |
0.107 |
3.227 |
0.411 |
3.268 |
0.533 |
2.204 |
4.331 |
-1 |
| Experimental |
|
|
|
3rd quintile |
9 |
5.333 |
0.236 |
3.778 |
0.521 |
3.465 |
0.894 |
1.681 |
5.250 |
9 |
5.333 |
0.236 |
3.778 |
0.521 |
3.694 |
0.973 |
1.751 |
5.637 |
0 |